Fetching the paper…
Reading the bibliography…
Recent researches on neural network have shown significant advantage in machine learning over traditional algorithms based on handcrafted features and models.
Exploring the Granularity of Sparsity in Convolutional Neural Networks. In Computer Vision and Pattern Recognition Workshops
Huizi Mao, Song Han, Jeff Pool, Wenshuo Li, Xingyu Liu, Yu Wang, and William J. Dally. 2017 · 1934
Earlier work this paper cites.
Arithmetic complexity of computations
Shmuel Winograd. 1980 · 1980
Earlier work this paper cites.
Efficient and accurate approximations of nonlinear convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Xiangyu Zhang, Jianhua Zou, Xiang Ming, Kaiming He, and Jian Sun. 2015b · 1992
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012 · 2012
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. 2014 · 2014
Earlier work this paper cites.
Deep speech: Scaling up end-to-end speech recognition
Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, et al · 2014
Earlier work this paper cites.
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014 · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman. 2014 · 2014
Earlier work this paper cites.
Compressing neural networks with the hashing trick. In International Conference on Machine Learning
Wenlin Chen, James Wilson, Stephen Tyree, Kilian Weinberger, and Yixin Chen. 2015 · 2015
Earlier work this paper cites.
Song Han, Huizi Mao, and William J Dally. 2015 · 2015
Earlier work this paper cites.
Sparse convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Baoyuan Liu, Min Wang, Hassan Foroosh, Marshall Tappen, and Marianna Pensky. 2015 · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. 2015 · 2015
Earlier work this paper cites.
Going deeper with convolutions. Cvpr
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, et al · 2015
Earlier work this paper cites.
Optimizing fpga-based accelerator design for deep convolutional neural networks. In Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
Chen Zhang, Peng Li, Guangyu Sun, Yijin Guan, Bingjun Xiao, and Jason Cong. 2015a · 2015
Earlier work this paper cites.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2016
Earlier work this paper cites.
Fused-layer CNN accelerators. In Microarchitecture (MICRO), 2016 49th Annual IEEE/ACM International Symposium on
Manoj Alwani, Han Chen, Michael Ferdman, and Peter Milder. 2016 · 2016
Earlier work this paper cites.
Deep speech 2: End-to-end speech recognition in english and mandarin. In International Conference on Machine Learning
Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Qiang Cheng, Guoliang Chen, et al · 2016
Earlier work this paper cites.
Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks. In Field-Programmable Technology (FPT), 2016 International Conference on
Roberto DiCecco, Griffin Lacey, Jasmina Vasiljevic, Paul Chow, Graham Taylor, and Shawki Areibi. 2016 · 2016
Earlier work this paper cites.
Accelerating datacenter workloads. In 26th International Conference on Field Programmable Logic and Applications (FPL)
PK Gupta. 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Earlier work this paper cites.
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer. 2016 · 2016
Earlier work this paper cites.
Fengfu Li, Bo Zhang, and Bin Liu. 2016b · 2016
Earlier work this paper cites.
A high performance FPGA-based accelerator for large-scale convolutional neural networks. In Field Programmable Logic and Applications (FPL), 2016 26th International Conference on
Huimin Li, Xitian Fan, Li Jiao, Wei Cao, Xuegong Zhou, and Lingli Wang. 2016a · 2016
Earlier work this paper cites.
Automatic code generation of convolutional neural networks in FPGA implementation. In Field-Programmable Technology (FPT), 2016 International Conference on
Zhiqiang Liu, Yong Dou, Jingfei Jiang, and Jinwei Xu. 2016b · 2016
Earlier work this paper cites.
Design space exploration of fpga-based deep convolutional neural networks. In Design Automation Conference (ASP-DAC), 2016 21st Asia and South Pacific
Mohammad Motamedi, Philipp Gysel, Venkatesh Akella, and Soheil Ghiasi. 2016 · 2016
Earlier work this paper cites.
Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC. In Field-Programmable Technology (FPT), 2016 International Conference on
Eriko Nurvitadhi, David Sheffield, Jaewoong Sim, Asit Mishra, Ganesh Venkatesh, and Debbie Marr. 2016 · 2016
Earlier work this paper cites.
Going deeper with embedded fpga platform for convolutional neural network. In Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
Jiantao Qiu, Jie Wang, Song Yao, Kaiyuan Guo, Boxun Li, Erjin Zhou, Jincheng Yu, Tianqi Tang, Ningyi Xu, Sen Song, et al · 2016
Earlier work this paper cites.
From high-level deep neural models to FPGAs. In Microarchitecture (MICRO), 2016 49th Annual IEEE/ACM International Symposium on
Hardik Sharma, Jongse Park, Divya Mahajan, Emmanuel Amaro, Joon Kyung Kim, Chenkai Shao, Asit Mishra, and Hadi Esmaeilzadeh. 2016 · 2016
Cited alongside, same era.
Overcoming resource underutilization in spatial CNN accelerators. In Field Programmable Logic and Applications (FPL), 2016 26th International Conference on
Yongming Shen, Michael Ferdman, and Peter Milder. 2016 · 2016
Cited alongside, same era.
Throughput-optimized OpenCL-based FPGA accelerator for large-scale convolutional neural networks. In Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
Naveen Suda, Vikas Chandra, Ganesh Dasika, Abinash Mohanty, Yufei Ma, Sarma Vrudhula, Jae-sun Seo, and Yu Cao. 2016 · 2016
Cited alongside, same era.
DeepBurning: automatic generation of FPGA-based learning accelerators for the neural network family. In Design Automation Conference (DAC), 2016 53nd ACM/EDAC/IEEE
Ying Wang, Jie Xu, Yinhe Han, Huawei Li, and Xiaowei Li. 2016 · 2016
Cited alongside, same era.
Scalable high-performance architecture for convolutional ternary neural networks on FPGA. In Field Programmable Logic and Applications (FPL), 2017 27th International Conference on
Adrien Prost-Boucle, Alban Bourge, Frédéric Pétrot, Hande Alemdar, Nicholas Caldwell, and Vincent Leroy. 2017 · 2017
Closest in time.
Customizing neural networks for efficient fpga implementation. In Field-Programmable Custom Computing Machines (FCCM), 2017 IEEE 25th Annual International Symposium on
Mohammad Samragh, Mohammad Ghasemzadeh, and Farinaz Koushanfar. 2017 · 2017
Closest in time.
Escher: A CNN Accelerator with Flexible Buffering to Minimize Off-Chip Transfer. In Proceedings of the 25th IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM’17). IEEE Computer Society, Los Alamitos, CA, USA
Yongming Shen, Michael Ferdman, and Peter Milder. 2017 · 2017
Closest in time.
Finn: A framework for fast, scalable binarized neural network inference. In Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
Yaman Umuroglu, Nicholas J Fraser, Giulio Gambardella, Michaela Blott, Philip Leong, Magnus Jahre, and Kees Vissers. 2017 · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Caffeine: Towards uniformed representation and acceleration for deep convolutional neural networks. In Computer-Aided Design (ICCAD), 2016 IEEE/ACM International Conference on
Chen Zhang, Zhenman Fang, Peipei Zhou, Peichen Pan, and Jason Cong. 2016a · 2016
Cited alongside, same era.
Energy-Efficient CNN Implementation on a Deeply Pipelined FPGA Cluster. In Proceedings of the 2016 International Symposium on Low Power Electronics and Design
Chen Zhang, Di Wu, Jiayu Sun, Guangyu Sun, Guojie Luo, and Jason Cong. 2016b · 2016
Cited alongside, same era.
DoReFa-Net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou. 2016 · 2016
Cited alongside, same era.
Chenzhuo Zhu, Song Han, Huizi Mao, and William J Dally. 2016 · 2016
Cited alongside, same era.
An OpenCL (TM) Deep Learning Accelerator on Arria 10
Utku Aydonat, Shane O’Connell, Davor Capalija, Andrew C Ling, and Gordon R Chiu. 2017 · 2017
Cited alongside, same era.
CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices. In Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture
Caiwen Ding, Siyu Liao, Yanzhi Wang, Zhe Li, Ning Liu, Youwei Zhuo, Chao Wang, Xuehai Qian, Yu Bai, Geng Yuan, et al · 2017
Cited alongside, same era.
FP-DNN: An Automated Framework for Mapping Deep Neural Networks onto FPGAs with RTL-HLS Hybrid Templates. In Field-Programmable Custom Computing Machines (FCCM), 2017 IEEE 25th Annual International Symposium on
Yijin Guan, Hao Liang, Ningyi Xu, Wenqiang Wang, Shaoshuai Shi, Xi Chen, Guangyu Sun, Wei Zhang, and Jason Cong. 2017a · 2017
Cited alongside, same era.
FPGA-based accelerator for long short-term memory recurrent neural networks. In Design Automation Conference (ASP-DAC), 2017 22nd Asia and South Pacific
Yijin Guan, Zhihang Yuan, Guangyu Sun, and Jason Cong. 2017b · 2017
Cited alongside, same era.
Closest in time.
fpgaConvNet: Automated Mapping of Convolutional Neural Networks on FPGAs. In Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
Stylianos I Venieris and Christos-Savvas Bouganis. 2017a · 2017
Closest in time.
Latency-driven design for FPGA-based convolutional neural networks. In Field Programmable Logic and Applications (FPL), 2017 27th International Conference on
Stylianos I Venieris and Christos-Savvas Bouganis. 2017b · 2017
Closest in time.
Skipnet: Learning dynamic routing in convolutional networks
Xin Wang, Fisher Yu, Zi-Yi Dou, and Joseph E Gonzalez. 2017 · 2017
Closest in time.
Automated Systolic Array Architecture Synthesis for High Throughput CNN Inference on FPGAs. In Proceedings of the 54th Annual Design Automation Conference 2017
Xuechao Wei, Cody Hao Yu, Peng Zhang, Youxiang Chen, Yuxin Wang, Han Hu, Yun Liang, and Jason Cong. 2017 · 2017
Closest in time.
A high-throughput reconfigurable processing array for neural networks. In Field Programmable Logic and Applications (FPL), 2017 27th International Conference on
Ephrem Wu, Xiaoqian Zhang, David Berman, and Inkeun Cho. 2017 · 2017
Closest in time.
Exploring Heterogeneous Algorithms for Accelerating Deep Convolutional Neural Networks on FPGAs. In Proceedings of the 54th Annual Design Automation Conference 2017
Qingcheng Xiao, Yun Liang, Liqiang Lu, Shengen Yan, and Yu-Wing Tai. 2017 · 2017
Closest in time.
Instruction driven cross-layer CNN accelerator with winograd transformation on FPGA. In International Conference on Field Programmable Technology
Jincheng Yu, Yiming Hu, Xuefei Ning, Jiantao Qiu, Kaiyuan Guo, Yu Wang, and Huazhong Yang. 2017 · 2017
Closest in time.
Frequency domain acceleration of convolutional neural networks on CPU-FPGA shared memory system. In Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
Chi Zhang and Viktor Prasanna. 2017 · 2017
Closest in time.
Improving the Performance of OpenCL-based FPGA Accelerator for Convolutional Neural Network.. In FPGA
Jialiang Zhang and Jing Li. 2017 · 2017
Closest in time.
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun. 2017 · 2017
Closest in time.
Accelerating Binarized Convolutional Neural Networks with Software-Programmable FPGAs.. In FPGA
Ritchie Zhao, Weinan Song, Wentao Zhang, Tianwei Xing, Jeng-Hau Lin, Mani B Srivastava, Rajesh Gupta, and Zhiru Zhang. 2017 · 2017
Closest in time.
https://github.com/Xilinx/chaidnn . ([n. d.])
[n. d.] · 2018
Closest in time.
https://www.xilinx.com/support/documentation/white_papers/wp504-accel-dnns.pdf . ([n. d.])
[n. d.] · 2018
Closest in time.
http://www.deephi.com/technology/dnndk . ([n. d.])
[n. d.] · 2018
Closest in time.
{ \{ TVM } \} : An Automated End-to-End Optimizing Compiler for Deep Learning. In 13th { \{ USENIX } \} Symposium on Operating Systems Design and Implementation ( { \{ OSDI } \} 18)
Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, et al · 2018
Closest in time.
LCP: a layer clusters paralleling mapping method for accelerating inception and residual networks on FPGA. In Proceedings of the 55th Annual Design Automation Conference
Xinhan Lin, Shouyi Yin, Fengbin Tu, Leibo Liu, Xiangyu Li, and Shaojun Wei. 2018 · 2018
Closest in time.
Towards a Uniform Template-based Architecture for Accelerating 2D and 3D CNNs on FPGA. In Acm/sigda International Symposium
Junzhong Shen, You Huang, Zelong Wang, Yuran Qiao, Mei Wen, and Chunyuan Zhang. 2018 · 2018
Closest in time.
Mnasnet: Platform-aware neural architecture search for mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, and Quoc V Le. 2018 · 2018
Closest in time.
Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions
Stylianos I Venieris, Alexandros Kouris, and Christos-Savvas Bouganis. 2018 · 2018
Closest in time.
Design Flow of Accelerating Hybrid Extremely Low Bit-width Neural Network in Embedded FPGA
Junsong Wang, Qiuwen Lou, Xiaofan Zhang, Chao Zhu, Yonghua Lin, and Deming Chen. 2018 · 2018
Closest in time.
A Fully Onchip Binarized Convolutional Neural Network FPGA Impelmentation with Accurate Inference. In Proceedings of the International Symposium on Low Power Electronics and Design
Li Yang, Zhezhi He, and Deliang Fan. 2018 · 2018
Closest in time.
DNNBuilder: an automated tool for building high-performance DNN hardware accelerators for FPGAs. In Proceedings of the International Conference on Computer-Aided Design
Xiaofan Zhang, Junsong Wang, Chao Zhu, Yonghua Lin, Jinjun Xiong, Wen-mei Hwu, and Deming Chen. 2018 · 2018
Closest in time.
Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs. In Proceedings of the 2018 on Great Lakes Symposium on VLSI
Chuanhao Zhuge, Xinheng Liu, Xiaofan Zhang, Sudeep Gummadi, Jinjun Xiong, and Deming Chen. 2018 · 2018
Closest in time.